Clean data:
plots_df = read_excel("./data/nta-metadata.xlsx", sheet = "NTA Data") %>%
janitor::clean_names() %>%
select(nta_name, nta_code, total_pop, hispanic:other_race, poverty, smm, gonorrhea, health_ins, edu_less_than_hs, preterm_births, late_no_prenatal_care, medicaid_enroll) %>%
drop_na(total_pop) %>%
mutate(poverty_level = cut(poverty, breaks = c(-Inf, 10, 20, 30, 40, Inf), labels = c("poverty_10","poverty_20", "poverty_30", "poverty_40", "poverty_40+"))) %>%
pivot_longer(
cols = hispanic:other_race,
names_to = "race",
values_to = "percent_pop",
values_drop_na = TRUE
)
poverty Vs SMM
poverty_smm_ggplot =
plots_df %>%
ggplot(aes(x = poverty, y = smm), group = nta_name) +
geom_point(color = "red")
ggplotly(poverty_smm_ggplot)
poverty2_smm_ggplot =
plots_df %>%
ggplot(aes(x = poverty_level, y = smm)) +
geom_boxplot()
ggplotly(poverty2_smm_ggplot)
## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
late or no prenatal care vs SMM
prenatal_care_ggplot =
plots_df %>%
ggplot(aes(x = late_no_prenatal_care, y = smm, group = nta_name)) +
geom_point(color = "red")
ggplotly(prenatal_care_ggplot)
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
gonorrhea vs health insurance
gonorrhea1_ggplot =
plots_df %>%
ggplot(aes(x = health_ins, y = gonorrhea), group = nta_name) +
geom_point(color = "green")
ggplotly(gonorrhea1_ggplot)
gonorrhea vs medicaid
gonorrhea2_ggplot =
plots_df %>%
ggplot(aes(x = medicaid_enroll, y = gonorrhea), group = nta_name) +
geom_point(color = "green")
ggplotly(gonorrhea2_ggplot)
gonorrhea vs education level
gonorrhea3_ggplot =
plots_df %>%
ggplot(aes(x = edu_less_than_hs, y = gonorrhea), group = nta_name) +
geom_point(color = "green")
ggplotly(gonorrhea3_ggplot)
health insurance vs preterm birth
preterm_ggplot =
plots_df %>%
ggplot(aes(x = health_ins, y = preterm_births), group = nta_name) +
geom_point(color = "blue")
ggplotly(preterm_ggplot)
medicaid enrollment vs preterm birth
preterm2_ggplot =
plots_df %>%
ggplot(aes(x = medicaid_enroll, y = preterm_births, group = nta_name)) +
geom_point(color = "blue")
ggplotly(preterm2_ggplot)
late or no prenatal care vs preterm births
prenatal_care_ggplot =
plots_df %>%
ggplot(aes(x = late_no_prenatal_care, y = preterm_births, group = nta_name)) +
geom_point(color = "blue")
ggplotly(prenatal_care_ggplot)